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Heat transfer prediction of supercritical water with artificial neural networks

W. Chang, X. Chu, A. Fareed, S. Pandey, J. Luo, B. Weigand, and E. Laurien. Applied thermal engineering, (2018)
DOI: 10.1016/j.applthermaleng.2017.12.063

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Tighter Dimensioning of Heterogeneous Multi-Resource Autonomous CPS with Control Performance Guarantees.D. Roy, W. Chang, S. Mitter, and S. Chakraborty. DAC, page 10. ACM, (2019)Thermal Sensing Using Micro-ring Resonators in Optical Network-on-Chip.W. Liu, M. Li, W. Chang, C. Xiao, Y. Xie, N. Guan, and L. Jiang. DATE, page 1611-1614. IEEE, (2019)Multi-Objective Co-Optimization of FlexRay-Based Distributed Control Systems.D. Roy, L. Zhang, W. Chang, D. Goswami, and S. Chakraborty. RTAS, page 279-290. IEEE Computer Society, (2016)Reliable CPS Design for Mitigating Semiconductor and Battery Aging in Electric Vehicles.W. Chang, A. Pröbstl, D. Goswami, M. Zamani, and S. Chakraborty. CPSNA, page 37-42. IEEE Computer Society, (2015)Control/Architecture Codesign for Cyber-Physical Systems.W. Chang, L. Zhang, D. Roy, and S. Chakraborty. Handbook of Hardware/Software Codesign, Springer, (2017)System architecture and software design for electric vehicles.M. Lukasiewycz, S. Steinhorst, S. Andalam, F. Sagstetter, P. Waszecki, W. Chang, M. Kauer, P. Mundhenk, S. Shreejith, S. Fahmy and 1 other author(s). DAC, page 95:1-95:6. ACM, (2013)A Dual-Mode Strategy for Performance-Maximisation and Resource-Efficient CPS Design.X. Dai, W. Chang, S. Zhao, and A. Burns. ACM Trans. Embedded Comput. Syst., 18 (5s): 85:1-85:20 (2019)Making smart contract development more secure and easier.M. Ren, F. Ma, Z. Yin, Y. Fu, H. Li, W. Chang, and Y. Jiang. ESEC/SIGSOFT FSE, page 1360-1370. ACM, (2021)Dimensioning and configuration of EES systems for electric vehicles with boundary-conditioned adaptive scalarization.W. Chang, M. Lukasiewycz, S. Steinhorst, and S. Chakraborty. CODES+ISSS, page 26:1-26:10. IEEE, (2013)Resource-aware Automotive Control Systems Design: A Cyber-Physical Systems Approach.W. Chang, and S. Chakraborty. Foundations and Trends in Electronic Design Automation, 10 (4): 249-369 (2016)

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Heat transfer prediction of supercritical water with artificial neural networks

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Heat transfer prediction of supercritical water with artificial neural networks

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Heat transfer prediction of supercritical water with artificial neural networks